Google BigQuery

Name: Google BigQuery
Author: bigquery

Run SQL queries on BigQuery datasets, list tables, and inspect schemas. Manage jobs, export results, and explore data warehouse resources.

该来源不提供完整文件导出（国内平台多为平台内托管），仅存元数据与原链

模型生成摘要（rules/v1 · 2026-09-27 15:58）：Run SQL queries on BigQuery datasets, list tables, and inspect schemas
这是模型对公开材料的总结，不是官方声明，请以原链内容为准。

接入信息

传输形态: http
鉴权方式: 鉴权未知
端点: https://bigquery.run.tools

鉴权方式未标注，请核对官方文档后再接入——不要直接使用以下片段

{
  "mcpServers": {
    "Google BigQuery": {
      "url": "https://bigquery.run.tools"
    }
  }
}

能力清单

工具	说明
list_dataset_ids	List BigQuery dataset IDs in a Google Cloud project.
get_dataset_info	Get metadata information about a BigQuery dataset.
list_table_ids	List table ids in a BigQuery dataset.
get_table_info	Get metadata information about a BigQuery table.
execute_sql	Run a SQL query in the project and return the result. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). The `execute_sql` tool can also have side effects if the the query invokes [remote functions](https://cloud.google.com/bigquery/docs/remote-functions) or [Python UDFs](https://cloud.google.com/bigquery/docs/user-defined-functions-python). All queries that are run using the `execute_sql` tool have a label that identifies the tool as the source. You can use this label to filter the queries using the label and value pair `goog-mcp-server: true`. Queries are charged to the project specified in the `project_id` field. Example Queries: -- Count the number of penguins in each island. SELECT island, COUNT() AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Evaluate a bigquery ML Model. SELECT FROM ML.EVALUATE(MODEL `my_dataset.my_model`) -- Evaluate BigQuery ML model on custom data SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Predict using BigQuery ML model: SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30)

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